Integrate Groq Chat Model with 500+ apps and services

What this template does This template serves as a Chatbot that enables you to ask questions about the content of a PDF directly in Telegream. It checks incoming Telegram messages if they contain a document. If they do, it stores the PDF in a Pinecone Vector store. If there's no document, it will search the Vector Store for information and try to answer your question. Setup Open the Telegram app and search for the BotFather user (@BotFather) Start a chat with the BotFather Type /newbot to create a new bot Follow the prompts to name your bot and get a unique API token Save your access token and username Once you set your bot, you can send the pdf, and then ask questions about the content. How to adjust it to your needs You can exchange the Groq chat model with any model that you like Exchange Pinecone with any other vector store tool you like (e.g. Supabase, Postgres or QDrant) #Telegram, #Pinecone, #Openai, #GroQ

How it works Fetch transaction notification emails (including attachments) Clean up data Let AI (Basic LLM Chain node) generate bookkeeping item Send to Google sheet Details The example fetch email from Gmail lables, suggested using filters to automatically orgianize email into the labels Data will send to "raw data" sheet Example google sheet: https://docs.google.com/spreadsheets/d/1_IhdHj8bxtsfH2MRqKuU2LzJuzm4DaeKSw46eFcyYts/edit?gid=1617968863#gid=1617968863

This n8n template combines an AI agent with n8n's multi-page forms to create a novel interaction which allows automated question-and-answer sessions. One of the more obvious use-cases of this interaction is what I'm calling the AI interviewer. You can read the full post here: https://community.n8n.io/t/build-your-own-ai-interview-agents-with-n8n-forms/62312 Live demo here: https://jimleuk.app.n8n.cloud/form/driving-lessons-survey How it works A form trigger is used to start the interview and a new session is created in redis to capture the transcript. An AI agent is then tasked to ask questions to the user regarding the topic of the interview. This is setup as a loop so the questions never stop unless the user wishes to end the interview. Each answer is recorded in our session set up earlier between questions. When the user requests to end the interview we break the loop and show the interview completion screen. Finally, the session is then saved in a Google Sheet which can then be shared with team members and for the purpose of data analysis. How to use You'll need to be on a n8n instance that is accessible to your target audience. Not technical enough to setup your own server? Try out n8n cloud and instantly deploy template! Remember to activate the workflow so the form trigger is published and available for users to use. Requirements Groq LLM for AI agent. Feel free to swap this out for any other LLM. Redis(-compatible) storage for capturing sessions Customising this workflow The next step would be adding tools! AI interviews with knowledge retrieval could definitely open up other possibilities. Eg. An onboarding wizard generating questions by pulling facts from internal knowledgebase.

This n8n template shows how anyone can build a simple newsletter-like subscription service where users can enrol themselves to receive messages/content on a regular basis. It uses n8n forms for data capture, Airtable for database, AI for content generation and Gmail for email sending. How it works An n8n form is setup up to allow users to subscribe with a desired topic and interval of which to recieve messages via n8n forms which is then added to the Airtable. A scheduled trigger is executed every morning and searches for subscribers to send messages for based on their desired intervals. Once found, Subscribers are sent to a subworkflow which performs the text content generation via an AI agent and also uses a vision model to generate an image. Both are attached to an email which is sent to the subscriber. This email also includes an unsubscribe link. The unsubscribe flow works similarly via n8n form interface which when submitted disables further scheduled emails to the user. How to use Make a copy of sample Airtable here: https://airtable.com/appL3dptT6ZTSzY9v/shrLukHafy5bwDRfD Make sure the workflow is "activated" and the forms are available and reachable by your audience. Requirements Airtable for Database OpenAI for LLM (but compatible with others) Gmail for Email (but can be replaced with others) Customising this workflow This simple use can be extended to deliver any types of content such as your company newsletter, promotions, social media posts etc. Doesn't have to be limited to just email - try social messaging, Whatsapp, Telegram and others.

AI-Powered Chatbot Workflow with MySQL Integration This guide shows you how to deploy a chatbot that lets you query your database using natural language. You will build a system that accepts chat messages, retains conversation history, constructs dynamic SQL queries, and returns responses generated by an AI model. By following these instructions, you will have a working solution that integrates n8n’s AI Agent capabilities with MySQL. Prerequisites Before you begin, ensure that you have the following: An active n8n instance (self-hosted or cloud) running version 1.50.0 or later. Valid MySQL credentials configured in n8n. API credentials for the Groq Chat Model (or your preferred AI language model). Basic familiarity with SQL and n8n node concepts such as chat triggers and memory buffers. Access to the n8n Docs on AI Agents for further reference. Workflow Setup 1. Chat Interface & Trigger When Chat Message Received** This node listens for incoming chat messages via a webhook. When a message arrives, it triggers the workflow immediately. 2. Conversation Memory Chat History** This memory buffer node stores the last 10 interactions. It supplies conversation context to the AI Agent, ensuring that responses consider previous messages. 3. AI Agent Core AI Agent (Tools Agent)** The AI Agent node orchestrates the conversation by receiving the chat input and conversation history. It dynamically generates SQL queries based on your requests and coordinates calls to external tools (such as MySQL nodes). 4. Database Interactions MySQL Node** This node executes the SQL query generated by the AI Agent. You reference the query using an expression (e.g., {{$node["AI Agent"].json.sql_query}}), allowing the agent’s output to control data retrieval. MySQL Schema Node** This node retrieves a list of base tables from your MySQL database (excluding system schemas). The agent uses this information to understand the available tables. MySQL Definition Node** This node fetches detailed metadata (such as column names, data types, and relationships) for a specific table. The table and schema names are supplied dynamically by the AI Agent. 5. Language Model Processing Groq Chat Model** This node connects to the Groq Chat API to generate text completions. It processes the combined input (chat message, context, and data fetched from MySQL) and produces the final response. 6. Guidance & Customization Sticky Notes** These nodes provide guidance on: Switching the chat model if you wish to use another provider (e.g., OpenAI or Anthropic). Adjusting the maximum token count per interaction. Customizing the SQL queries and the context window size. They help you modify the workflow to suit your environment and requirements. Workflow Connections The Chat Trigger passes the incoming message to the AI Agent. The Chat History node supplies conversation context to the AI Agent. The AI Agent calls the MySQL nodes as external tools, generating and sending dynamic SQL queries. The Groq Chat Model processes the consolidated input from the agent and outputs the natural language response delivered to the user. Testing the Workflow Send a chat message using the chat interface. Observe how the AI Agent processes the input and generates a corresponding SQL query. Verify that the MySQL nodes execute the query and return data. Confirm that the Groq Chat Model produces a coherent natural language response. Refer to the sticky notes for guidance if you need to fine-tune any node settings. Next Steps and References Customize Your AI Model** Replace the Groq Chat Model with another language model (such as the OpenAI Chat Model) by updating the node credentials and configuration. Enhance Memory Settings** Adjust the Chat History node’s context window to retain more or fewer messages based on your needs. Modify SQL Queries** Update the SQL queries in the MySQL nodes to match your specific database schema and desired data. Further Reading** Consult the n8n Docs on AI Agents for additional details and examples to expand your workflow’s capabilities. Set Up a Website Chatbot** Copy & Paste and replace the placeholders in the following code to embed the chatbot into your personal or company's website: View in CodePen 🡥 By following these steps, you will deploy a robust AI chatbot workflow that integrates with your MySQL database, allowing you to query data using natural language.

AI-Powered Tech Radar Advisor This project is built on top of the famous open source ThoughtWorks Tech Radar. You can use this template to build your own AI-Powered Tech Radar Advisor for your company or group of companies. Target Audience This template is perfect for: Tech Audit & Governance Leaders:** Those seeking to build a tech landscape AI platform portal. Tech Leaders & Architects:** Those aiming to provide modern AI platforms that help others understand the rationale behind strategic technology adoption. Product Managers:** Professionals looking to align product innovation with the company's current tech trends. IT & Engineering Teams:** Teams that need to aggregate, analyze, and visualize technology data from multiple sources efficiently. Digital Transformation Experts:** Innovators aiming to leverage AI for actionable insights and strategic recommendations. Data Analysts & Scientists:** Individuals who want to combine structured SQL analysis with advanced semantic search using vector databases. Developers:** Those interested in integrating RAG chatbot functionality with conversation storage. 1. Description Tech Constellation is an AI-powered Tech Radar solution designed to help organizations visualize and steer their technology adoption strategy. It seamlessly ingests data from a Tech Radar Google Sheet—converting it into both a MySQL database and a vector index—to consolidate your tech landscape in one place. The platform integrates an interactive AI chat interface powered by four specialized agents: AI Agent Router:** Analyzes and routes user queries to the most suitable processing agent. SQL Agent:** Executes precise SQL queries on structured data. RAG Agent:** Leverages semantic, vector-based search for in-depth insights. Output Guardrail Agent:** Validates responses to ensure they remain on-topic and accurate. This powerful template is perfect for technology leaders, product managers, engineering teams, and digital transformation experts looking to make data-driven decisions aligned with strategic initiatives across groups of parent-child companies. 2. Features Data Ingestion A Google Sheet containing tech radar data is used as the primary source. The data is ingested and converted into a MySQL database. Simultaneously, the data is indexed into a vector database for semantic (vector-based) search. Interactive AI Chat Chat Integration:** An AI-powered chat interface allows users to ask questions about the tech radar. Customizable AI Agents:** AI Agent Router: Determines the query type and routes it to the appropriate agent. SQL Agent: Processes queries using SQL on structured data. RAG Agent: Performs vector-based searches on document-like data. Output Guardrail Agent: Validates queries and ensures that the responses remain on-topic and accurate. Usage Examples Tell me, is TechnologyABC adopted or on hold, and why? List all the tools that are considered part of the strategic direction for company3 but are not adopted. Project Links & Additional Details GitHub Repository (Frontend Interface Source Code):** github.com/dragonjump/techconstellation Try It:** https://scaler.my